Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study | Zendy

Celia Álvarez-Romero | Zendy; Alicia Martínez-García | Zendy; Jara Ternero Vega | Zendy; Pablo Díaz-Jiménez | Zendy; Carlos Jimènez-Juan | Zendy; Maria Dolores NietoMartín | Zendy; Esther Román-Villarán | Zendy; Tomi Kovačević | Zendy; Darijo Bokan | Zendy; Sanja Hromiš | Zendy; Jelena Djekić Malbaša | Zendy; Suzana Beslać | Zendy; Bojan Zarić | Zendy; Mert Gençtürk | Zendy; Ali Anıl Sınacı | Zendy; Manuel Ollero Baturone | Zendy; Carlos Luís Parra-Calderón | Zendy

Open Access

Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study

Author(s) -

Celia Álvarez-Romero,

Alicia Martínez-García,

Jara Ternero Vega,

Pablo Díaz-Jiménez,

Carlos Jimènez-Juan,

Maria Dolores NietoMartín,

Esther Román-Villarán,

Tomi Kovačević,

Darijo Bokan,

Sanja Hromiš,

Jelena Djekić Malbaša,

Suzana Beslać,

Bojan Zarić,

Mert Gençtürk,

Ali Anıl Sınacı,

Manuel Ollero Baturone,

Carlos Luís Parra-Calderón

Publication year - 2022

Publication title -

jmir medical informatics

Language(s) - English

Resource type - Journals

ISSN - 2291-9694

DOI - 10.2196/35307

Subject(s) - interoperability , computer science , health care , observational study , medicine , artificial intelligence , knowledge management , world wide web , pathology , economics , economic growth

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